Modeling of mPGES-1 three-dimensional structures: applications in drug design and discovery

ABSTRACT

This invention relates to representations of prostaglandin synthase three-dimensional structures. Such representations are suitable for designing agents that modulate the activity of the enzyme by binding to the substrate binding domain.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.11/698,230, filed Jan. 24, 2007, the entire disclosure of which isincorporated herein by reference.

TECHNICAL FIELD

Computational methods for determining the three-dimensional structure ofone or more polypeptides are provided. Also provided arethree-dimensional models of a microsomal prostaglandin synthase moleculeand computer-implemented methods for identifying compounds that interactwith the molecule.

BACKGROUND

Prostaglandin (PG) E2 is produced by a variety of cells and tissues andexhibits potent diverse bioactivities. Its production is mediated bythree enzymatic reactions involving phospholipase A2 (PLA2),cyclooxygenase (COX), and PGE2 synthase (PGES). In this biosyntheticpathway, arachidonic acid (AA) releases from membrane phospholipids bycytosolic or secretory PLA2 and is converted to prostaglandin H2 (PGH2)by COXs. PGH2 is then isomerized to prostaglandin E2 (PGE2) by terminalPGES enzymes. PGES enzymes, that lie downstream of COXs, occur in threeforms in mammalian cells. Among them, the microsomal and membrane-boundsynthase (namely mPGES-1) has received much more attention andestablished as a novel drug target in the areas of inflammation,tumorigenesis, and bone disorders. Hence, mPGES-1 is involved in anumber of diseases including arthritis, burn injury and pain diseases,atherosis, cancer, and even the exacerbation of Alzheimer's disease.Recently reported studies have led to the characterization of itsinducible distribution, expression, enzymatic kinetics, and biologicaland pathological functions. The expression of mPGES-1 is up-regulated bypro-inflammatory stimuli and down-regulated by anti-inflammatoryglucocorticoids, often in accordance with that of COX-2. The proteinmPGES-1 has been identified as the central switch during immune-inducedpyresis, and deletion of mPGES-1 would reduce inducible and basal PGE2production and alter the gastric prostanoid profile. Compared to itsup-stream enzymes, inhibition of mPGES-1 does not block normal functionsof other PGs and, therefore, lacks the unexpected side effects producedby the inhibition of COXs, making it more attractive for the developmentof potential therapeutics, especially for the treatment ofinflammation-related diseases. However, no clinically useful inhibitorof mPGES-1 has been identified. To date, only two types of compounds,i.e. the COX-2 inhibitor NS-398 and 5-lipoxygenese-activating protein(FLAP) inhibitor MK-886 (see FIG. 9) and similar compounds (see e.g.,Riendeau et al., Bioorg. Med. Chem. Lett., 15:3352-3355), have beenfound to be able to inhibit mPGES-1. None of these compounds isselective for mPGES-1. It is highly desirable to develop more potent andselective inhibitors of mPGES-1 based on the structure and function ofthe enzyme for development of the next-generation therapeutics.

Initially, mPGES-1 was discovered as recombinant human microsomalglutathione-S-transferase (GST)-1-like 1 (MGST1-L1) and recognized as amember of membrane-associated proteins involved in eicosanoid andglutathione (GSH) metabolism (MAPEG) superfamily. It shows significanthomology with other MAPEG proteins, especially with the nearestsubfamily member MGST1. Hydropathy analysis suggests that all the MAPEGproteins have similar three-dimensional and membrane-spanningtopological properties. Site-directed mutagenesis revealed that R110 hasan essential role in the catalytic function of mPGES-1, whereas themutation on either R51 or R70 did not affect the activity.Unfortunately, further structure-function investigation is restrained bythe lack of the detailed three-dimensional structure of thismembrane-bound protein, making the structure-based design of drugstargeting mPGES-1 difficult. A two-dimensional (2D) electron projectionmap (with a resolution of 10 Å) of mPGES-1 revealed a trimer structure(Thoren, et al., J. Biol. Chem. 2003, 278, 22199-22209) which is verysimilar to that of MGST1, but the resolution of 10 Å is insufficient forthe purpose of building a three-dimensional model of mPGES-1.

Accordingly, more precise models of the three-dimensional structure ofmPGES-1 are needed so that potent and selective modulators of mPGES-1activity can be identified.

SUMMARY

Provided herein are three-dimensional structures of the substratebinding domain (SBD) of the microsomal prostaglandin E2 synthase-1(mPGES-1), and three-dimensional structures of mPGES-1 trimers, usefulfor designing and identifying compounds that modulate the activity ofthe synthase. Also provided are novel methods for generating a set ofcandidate structures of mPGES-1, the mPGES-1 substrate binding domain(SBD) and mPGES-1 trimers. Also provided are methods of identifyingcompounds that bind to an mPGES-1 structure provided herein, includingthose that bind to the SBD of mPGES-1.

Accordingly, in one embodiment a method for identifying a set ofcandidate structures includes a) obtaining a first amino acid sequencederived from a query polypeptide; b) obtaining a second amino acidsequence derived from a template polypeptide, wherein the secondsequence comprises: i) a predetermined three-dimensional structure; andii) at least 50% sequence homology with the first sequence; c)performing a sequence alignment between the first sequence and thesecond sequence, and identifying common secondary structures; d)generating a plurality of candidate topological structures by applyingpredetermined geometric parameters to the secondary structures andtransforming each topological structure into the amino acid residuesassociated with the secondary structures; e) generating a firstconformation set by screening the plurality of candidate topologicalstructures with the predetermined geometric parameters and identifyingthe structures that correspond to the parameters; f) generating a secondconformation set by applying energy minimization functions to the firstconformation set and identifying energetically-favored conformations;and g) generating a final conformation set by selecting those structuresthat exhibit an energy gradient having a root mean square deviation(RMSD) of less than 0.001 kcal mol⁻¹ Å⁻¹, wherein the final conformationset represents the set of candidate structures of the query polypeptide.

In some embodiments, methods of identifying a set of candidatestructures of a polypeptide further include generating the sequencealignment by generating the reciprocal position of the conservedresidues. In other embodiments, such methods further include modelingthe interaction of each member of the final conformation set with atleast one substrate, wherein the modeling comprises molecular dockingusing binding site searching and/or interaction energy scoring; andidentifying amino acid residues associated with the SBD that interactwith the substrate.

In other embodiments, the methods further include identifying at leastone amino acid residue that interacts with the substrate and determiningthe effect of the modification on substrate binding to the modifiedpolypeptide. In general the modification is a substitution, such as aconservative or non-conservative amino acid substitution. In yet anotherembodiment, the methods further include producing the modifiedpolypeptide in vivo or in vitro and assaying the activity of themodified polypeptide in vivo or in vitro.

In one aspect, a query polypeptide includes membrane-spanning regions ofamino acids. Such polypeptides include the membrane-associated proteinsinvolved in eicosanoid and glutathione metabolism (MAPEG). In otheraspects, the query polypeptide is microsomal prostaglandin E synthase-1(mPGES-1).

In another aspect, the template polypeptide is a member of themembrane-associated proteins involved in eicosanoid and glutathionemetabolism (MAPEG), such as microsomal glutathione-S-transferase-1(MGST-1). Structural parameters can include coordinates derived from 3Delectron projection maps of a template polypeptide, such as MGST1. Insome aspects, the structural parameters further include coordinatesderived from 2D electron projection maps of the query polypeptide, suchas mPGES-1. Structural parameters can correspond to the coordinates setforth in Table 1.

In another embodiment, a representation of a three-dimensional structureof the mPGES-1 substrate binding domain (SBD) is provided. Therepresentation is characterized in that: a) amino acid residues Q36,R110, T114, Y130, and Q134 of mPGES-1 are associated with thePGH2-binding site of the SBD; b) amino acid residue Y130 of mPGES-1 areassociated with the peroxy head of prostaglandin H2 (PGH2) when PGH2occupies at least a portion of the binding site; c) amino acid residueY130 of mPGES-1 is associated with the —SH group of glutathione (GSH)when GSH occupies at least a portion of the binding site; d) amino acidresidues R110, T114, and Q36 of mPGES-1 are associated with the carboxyltail of PGH2; e) the calculated binding free energy (□G) for an SBD-PGH2complex is between −5.0 kcal/mol and −9.0 kcal/mol; and f) thecalculated binding free energy (□G) for an SBD-GSH complex is between−4.0 kcal/mol and −8.0 kcal/mol.

In another embodiment, a representation of a three-dimensional structureof an mPGES-1 trimer is provided. The representation is characterized inthat: a) each monomer of the trimer comprises a representation of athree-dimensional structure of the mPGES-1 substrate binding domain(SBD); b) the trimer comprises C₃-fold symmetry; and c) therepresentation of the trimer comprises a homology model based on thecrystallographic structure of subunit 1 of cytochrome c oxidase.

In yet another embodiment, a method of structure-based identification ofcandidate compounds for regulation of interactions of mPGES-1 with itscognate ligands, is provided. The method includes a) providing a threedimensional structure of mPGES-1, the three dimensional structure beingselected from the group consisting of: i) the mPGES-1 substrate bindingdomain as set forth in claim 23; and ii) the mPGES-1 trimer as set forthin claim 25; b) identifying at least one candidate compound forinteracting with the three dimensional structure of a) and performingstructure based drug design.

In another embodiment, a machine-readable medium embedded withinformation that corresponds to the three-dimensional structuralrepresentation of the mPGES-1 substrate binding domain (SBD), isprovided. Also provided is a machine-readable medium embedded withinformation that corresponds to the three-dimensional structuralrepresentation of the mPGES-1 trimer.

In one embodiment, a computer system including a representation of thethree-dimensional structure of the mPGES-1 substrate binding domain(SBD) and a user interface to view the representation, is provided. Alsoprovided is a computer system that includes a representation of thethree-dimensional structure of the mPGES-1 trimer and a user interfaceto view the representation.

The various methods and computer-generated structures provided hereinare suitable for use in conducting a biotechnology business. Such abusiness can include identifying one or more candidate compounds forregulation of interactions of mPGES-1 with its cognate ligands,generating a machine-readable medium, or data signal embodied in acarrier wave, embedded with information that corresponds to thethree-dimensional structural representation of the candidate compoundand providing the medium or data signal to an end user.

In general, structures derived from the computer-generated modelsprovided herein encompass structures having coordinates that differ by aroot mean square deviation (RMSD) of less than about 1.5 Å, 0.75 Å, or0.35 Å, or any deviation in this range. In some aspects, the querypolypeptide includes an amino acid sequence having at least 75%, atleast 85%, or at least 95%, or any percent in this range, amino acidsequence identity to the template polypeptide.

In other embodiments, a structure of a synthase molecule provided hereinalso includes a ligand complexed with the synthase molecule. In someaspects, the ligand is a small molecule.

The details of one or more embodiments of the disclosure are set forthin the accompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 depicts a sequence alignment of the mPGES-1 with MGST1. Starsrefer to identical residues, whereas filled period or double filledperiod refer to conservative substitutions. All these positions (withstars and filled periods) give the total homology of mPGES-1 SEQ. I.D.No.: 12 with MGST1 SEQ. I.D. No.: 13 as 73%. Helices of mPGES-1 arelabeled. Mutated residues are numbered below the sequence.

FIG. 2 depicts the second set of 1934 conformations clustered into fourgroups based on their energies and C-alpha root mean square deviation(RMSD) values relative to the initial topological model. Group I (1232candidates) was discarded due to their positive potential energies,whereas groups II (285 candidates), III (286 candidates) and IV (131candidates) were used to derive the final set of 27 candidates. Theselected 27 candidates are shown as triangles.

FIG. 3 depicts conformational root-mean square deviation (RMSD) from theinitial topological model for the finally selected 27 candidates of theSBD model of mPGES-1.

FIG. 4A depicts a top view from outside of the membrane of an optimizedcomplex model of the SBD of mPGES-1 binding with substrates PGH2 andGSH. The SBD of mPGES-1 is represented as ribbon, and the two substratesare shown in stick.

FIG. 4B depicts PGH2 binding with the enzyme. Residues in the SBD ofmPGES-1 within 5 Å around PGH2 are shown and labeled in stick, theelectrostatic interaction is represented as the plus (+) and minus (−)signs, and the hydrogen bonding is indicated with dashed line.

FIG. 4C depicts GSH binding with the SBD of mPGES-1. Residues in the SBDof mPGES-1 within 5 Å around GSH are shown and labeled.

FIG. 5 depicts the cell membrane portion of mPGES-1 expression in E.coli. Bars represent the percentage of expression for the five mutants(Q36E, R110T, T114V, Y1301, and Q134E) relative to the wild-type (WT) ofmPGES-1.

FIG. 6 depicts the relative enzymatic activity of mPGES-1 and itsmutants. The relative activity is obtained by normalization from itsexpression level in FIG. 5 and the wild-type served as a standard of 100units.

FIG. 7 depicts experimentally measured K_(M) of mPGES-1 and its mutants.

FIG. 8 depicts the calculated K_(d) values of PGH2 binding withwild-type mPGES-1 and its mutants in comparison with the experimentallyderived K_(M).

FIG. 9 depicts the chemical structures of PGH2, PGE2, and COX-2inhibitors NS-398 and 5-lipoxygenese-activating protein (FLAP) inhibitorMK-886.

FIG. 10 depicts a flow diagram of an exemplary “ab initio” rationale forgenerating three-dimensional models of polypeptides.

FIG. 11A depicts an exemplary view of three-dimensional model #1obtained for the mPGES-1 trimer.

FIG. 11B depicts another an exemplary view of three-dimensional model #1obtained for the mPGES-1 trimer.

FIG. 11C depicts yet another exemplary view of three-dimensional model#1 obtained for the mPGES-1 trimer.

FIG. 12 depicts a sequence alignment of human mPGES-1 (SEQ ID NO:12)with the cytochrome c template (SEQ ID NO:11). The alpha-helices areunderlined.

FIG. 13A and FIG. 13B depict three-dimensional model #2 of the mPGES-1trimer complexed with an inhibitor (i.e. MK-886) in each substratebinding domain (SBD).

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Three-dimensional (3D) representations, methods, and computer programsfor the ab initio prediction of three-dimensional structures of proteinsare provided. Specifically, provided herein are representations of novelthree-dimensional structures of a microsomal prostaglandin E synthase-1(mPGES-1) molecule, and the structure of the mPGES-1 trimer. Alsoprovided are methods of generating such representations and methods ofusing such information to identify, design and/or modify compounds thatmodulate the activity of an mPGES-1 molecule. In addition, computersystems that include such information are provided.

Throughout the present disclosure the term “microsomal prostaglandin Esynthase-1 (mPGES-1) molecule” or “synthase molecule” are used describevarious embodiments of the inventions. It is understood that these termsencompass a single molecule of mPGES-1 (e.g., a monomer), fragments ofmPGES-1 (e.g., the substrate binding domain (SBD)), and/or multimers ofmPGES-1 (e.g., an mPGES-1 trimer).

Methods of Identifying Sets of Conformations

Provided herein are computer-implemented methods for homology modelingby comparing the amino acid sequence of a query polypeptide (e.g.,mPGES-1) with the amino acid sequence of a template polypeptide (e.g., amembrane-associated protein involved in eicosanoid and glutathionemetabolism (MAPEG)). The query polypeptide shares sequence homology withthe template polypeptide. Structural information associated with thetemplate polypeptide (e.g., atomic coordinates based on NMR or x-raycrystallographic data) can be used to generate a model of the querypolypeptide. The model can be further refined by subjecting thepreliminary model to energy minimization to yield an energy minimizedmodel and remodeling regions of the energy minimized model wherestereochemistry restraints are violated. These refinements yield sets ofconformations that can be further subjected to, for example, in silicointeractions with a suitable substrate.

The term “protein” is understood to include the terms “polypeptide” and“peptide” (which, at times, may be used interchangeably herein) withinits meaning. In addition, proteins comprising multiple polypeptidesubunits (e.g., dimers, trimers or tetramers), as well as othernon-proteinaceuos catalytic molecules will also be understood to beincluded within the meaning of “protein” as used herein. Similarly,“protein fragments,” i.e., stretches of amino acid residues thatcomprise fewer than all of the amino acid residues of a protein, arealso within the scope of the invention and may be referred to herein as“proteins.” Additionally, “protein domains” are also included within theterm “protein.” A “protein domain” represents a portion of a proteincomprised of its own semi-independent folded region having its owncharacteristic spherical geometry with hydrophobic core and polarexterior.

The methods provided herein can be employed in those cases where asequence comparison indicates possible local structural similarity ofthe query protein to protein(s) of known structure. For example, a smallstructural motif (a long helix, helical hairpin, fragment of abeta-sheet) can be used as a modeling “template”. Such a template canprovide a folding scaffold, thereby reducing the conformational space tobe searched in order to assemble the remaining portions of the structureof the query protein.

The structural motif can be associated with, for example, a “functionalsite.” The term “functional site” or “functional domain” generally referto any site in a protein that confers a function on the protein.Representative examples include active sites (i.e., those sites incatalytic proteins where catalysis occurs), protein-protein interactionsites, sites for chemical modification (e.g., glycosylation andphosphorylation sites), and ligand binding sites. Ligand binding sitesinclude, but are not limited to, metal binding sites, co-factor bindingsites, antigen binding sites, substrate channels and tunnels, andsubstrate binding domains (SBD). In an enzyme, a ligand binding sitethat is a substrate binding domain may also be an active site.Functional sites may also be composites of multiple functional sites,wherein the absence of one or more sites comprising the compositeresults in a loss of function. Identifying compounds that bind to afunctional site, such as a substrate binding domain, are discussedbelow.

Accordingly, in one embodiment a method for identifying a set ofcandidate structures includes a) obtaining a first amino acid sequencederived from a query polypeptide; b) obtaining a second amino acidsequence derived from a template polypeptide, wherein the secondsequence comprises: i) a predetermined three-dimensional structure; andii) at least 50% sequence homology with the first sequence; c)performing a sequence alignment between the first sequence and thesecond sequence, and identifying common secondary structures; d)generating a plurality of candidate topological structures by applyingpredetermined geometric parameters to the secondary structures andtransforming each topological structure in to the amino acid residuesassociated with the secondary structures; e) generating a firstconformation set by screening the plurality of candidate topologicalstructures with the predetermined geometric parameters and identifyingthe structures that correspond to the parameters; f) generating a secondconformation set by applying energy minimization functions to the firstconformation set and identifying energetically-favored conformations;and g) generating a final conformation set by selecting those structuresthat exhibit an energy gradient having a root mean square deviation(RMSD) of less than 0.001 kcal mol-1 Å-1, wherein the final conformationset represents the set of candidate structures of the query polypeptide.

Identifying a set of candidate structures is based, in part, on computergenerated structures derived, in part, from crystal structures ofhomologous proteins (i.e., “homologs”). As used herein, the term“homolog” refers to the polypeptide molecule, or a functional domainfrom said polypeptide from a first source having at least about 30%, 40%or 50% sequence identity, or at least about 60%, 70% or 75% sequenceidentity, or at least about 80% sequence identity, or more preferably atleast about 85% sequence identity, or even more preferably at leastabout 90% sequence identity, and most preferably at least about 95%, 97%or 99% amino acid sequence identity with the polypeptide, or anyfunctional domain thereof, from a second source. The second source maybe a version of the molecule from the first source that has beengenetically altered by any available means to change the primary aminoacid or may be from the same or a different species than that of thefirst source.

As previously mentioned, a template polypeptide includes a“predetermined three-dimensional structure.” As used herein, a“predetermined three-dimensional structure” includes crystalline formsof a polypeptide provided as data in the form of structure coordinates.As used herein, the term “atomic coordinates” or “structure coordinates”refers to mathematical coordinates that describe the positions of atomsin a crystal in a Protein Data Bank (PDB) format, including X, Y, Z andB, for each atom. The diffraction data obtained from the crystals areused to calculate an electron density map of the repeating unit of thecrystal. The electron density maps may be used to establish thepositions (i.e., coordinates X, Y and Z) of the individual atoms withinthe crystal.

The computer-generated structure coordinates identified for a querypolypeptide, or sets or polypeptides, based upon the coordinatesavailable from a template polypeptide, or an active site thereof, definea unique configuration of points in space. Those of skill in the artunderstand that a set of structure coordinates for a polypeptide, or apolypeptide complexed with a chemical entity, or a portion thereof,define a relative set of points that, in turn, define a configuration inthree dimensions. A similar or identical configuration can be defined byan entirely different set of coordinates, provided the distances andangles between coordinates remain essentially the same. Accordingly, thecoordinates provide a “scalable” configuration of points that can bemodified by increasing or decreasing the distances between coordinatesby a scalar factor while keeping the angles essentially the same.

For example, in identifying sets of conformationally suitablestructures, it may be desirable to identify “conformational” or“secondary” constraints. These terms refer to the presence of aparticular protein conformation, for example, an alpha-helix, paralleland anti-parallel beta strands, leucine zipper, zinc finger, etc. inwhich an amino acid residue, or group of residues, is located. Inaddition, conformational or secondary constraints can include amino acidsequence information without additional structural information. As anexample, “-C-X-X-C-” is a conformational constraint indicating that twocysteine residues must be separated by two other amino acid residues,the identities of each of which are irrelevant in the context of thisparticular constraint.

An “identity constraint” refers to a constraint that indicates theidentity of a particular amino acid residue at a particular amino acidposition in a protein. Typically, an amino acid position is determinedby counting from the amino-terminal residue of the protein up to andincluding the residue in question. As those in the art will appreciate,comparison between related proteins may reveal that the identity of aparticular amino acid residue at a given amino acid position in aprotein is not entirely conserved, i.e., different amino acid residuesmay be present at a particular amino acid position in related proteins,or even in allelic or other variants of the same protein.

In another embodiment, methods of identifying a set of candidatestructures of a polypeptide further include modeling the interaction ofeach member of a final conformation set with at least one “substrate” or“cognate ligand.” Such modeling can be in the form of molecular dockingusing binding site searching and/or interaction energy scoring. Theamino acid residues associated with the SBD, and that interacts with thesubstrate, can be identified.

A “functional site” refers to any site in a protein that has a function.Representative examples include active sites (i.e., those sites incatalytic proteins where catalysis occurs), protein-protein interactionsites, sites for chemical modification (e.g., glycosylation andphosphorylation sites), and substrate binding sites. Substrate bindingsites include, but are not limited to, metal binding sites, co-factorbinding sites, antigen binding sites, substrate channels and tunnels,and ligand binding sites. In an enzyme, a substrate binding site mayalso be an active site.

The methods provided herein include using a known ligand that binds toboth the query polypeptide and template polypeptide in order to furtherrefine the structure of the query polypeptide. Accordingly, thestructure of the substrate binding domain (SBD) of an mPGES-1 moleculecan be delineated using mPGES-1 cognate ligands (e.g., PGH2 and GSH).

Active sites, such as substrate binding domains, are of significantutility in the identification of compounds that specifically interactwith, and modulate the activity of, a particular polypeptide. Theassociation of natural ligands or substrates with the active sites oftheir corresponding receptors or enzymes is the basis of many biologicalmechanisms of action. Similarly, many compounds exert their biologicaleffects through association with the active sites of receptors andenzymes. Such associations may occur with all or any parts of the activesite. An understanding of such associations helps lead to the design ofcompounds that modulate the activity of their target. Therefore, thisinformation is valuable in designing potential modifiers of mPGES-1activity, as discussed in more detail below.

In other embodiments, the methods further include identifying at leastone amino acid residue that interacts with the substrate, modifying theresidue, and determining the effect of the modification on substratebinding to the modified polypeptide. In general the modification is asubstitution, such as a conservative or non-conservative amino acidsubstitution. It may be desirable to make mutations in the active siteof a polypeptide, e.g., to increase, reduce or completely eliminatesynthase activity. Mutations that will reduce or completely eliminatethe activity of mPGES-1 are provided in the examples below. Suchmutations can be introduced into a computer generated structuralrepresentation of a molecule. Such “in silico” mutagenesis can be usedto confirm or augment the computer generated structural representationof, for example, the mPGES-1 molecule. In vivo and in vitro mutagenesiscan be used to further confirm or augment the information generated insilico. Such mutations are discussed further in the examples providedbelow.

In yet another embodiment, the methods further include producing themodified polypeptide in vivo or in vitro and assaying the activity ofthe modified polypeptide in vivo or in vitro. Methods of producingmodified polypeptides in vivo or in vitro are well known to the skilledartisan. Examples of such methods are provided below.

In general amino acid modifications include substitutions of one aminoacid for another. Such substitutions, whether manufactured in silico, invitro, or in vivo, generally include conservative and non-conservativeamino acid substitutions.

As used herein, an “amino acid” is a molecule having the structurewherein a central carbon atom (the alpha-carbon atom) is linked to ahydrogen atom, a carboxylic acid group (the carbon atom of which isreferred to herein as a “carboxyl carbon atom”), an amino group (thenitrogen atom of which is referred to herein as an “amino nitrogenatom”), and a side chain group, R. When incorporated into a peptide,polypeptide, or protein, an amino acid loses one or more atoms of itsamino and carboxylic groups in the dehydration reaction that links oneamino acid to another. As a result, when incorporated into a protein, anamino acid is referred to as an “amino acid residue.” In the case ofnaturally occurring proteins, an amino acid residue's R groupdifferentiates the 20 amino acids from which proteins are synthesized,although one or more amino acid residues in a protein may be derivatizedor modified following incorporation into protein in biological systems(e.g., by glycosylation and/or by the formation of cysteine through theoxidation of the thiol side chains of two non-adjacent cysteine aminoacid residues, resulting in a disulfide covalent bond that frequentlyplays an important role in stabilizing the folded conformation of aprotein, etc.). As those in the art will appreciate, non-naturallyoccurring amino acids can also be incorporated into proteins,particularly those produced by synthetic methods, including solid stateand other automated synthesis methods. Examples of such amino acidsinclude, without limitation, alpha-amino isobutyric acid, 4-aminobutyric acid, L-amino butyric acid, 6-amino hexanoic acid, 2-aminoisobutyric acid, 3-amino propionic acid, ornithine, norlensine,norvaline, hydroxproline, sarcosine, citralline, cysteic acid,t-butylglyine, t-butylalanine, phenylylycine, cyclohexylalanine,beta-alanine, fluoro-amino acids, designer amino acids (e.g.,beta-methyl amino acids, alpha-methyl amino acids, alpha-methyl aminoacids) and amino acid analogs in general. In addition, when analpha-carbon atom has four different groups (as is the case with the 20amino acids used by biological systems to synthesize proteins, exceptfor glycine, which has two hydrogen atoms bonded to the carbon atom),two different enantiomeric forms of each amino acid exist, designated Dand L. In mammals, only L-amino acids are incorporated into naturallyoccurring polypeptides. Of course, the instant invention envisionsproteins incorporating one or more D- and L-amino acids, as well asproteins comprised of just D- or L-amino acid residues.

Conventional amino acid residue abbreviations are used throughout thispatent, and both the one and three letter codes are reproduced here forconvenience: alanine=“A” or “Ala”; arginine=“R” or “Arg”; asparagine=“N”or “Asn”; aspartic acid=“D” or “Asp”; cysteine=“C” or “Cys”; glutamicacid=“E” or “Glu” glutamine=“Q” or “Gln”; glycine=“G” or “Gly”;histidine=“H” or “His”; isoleucine=“I” or “Ile”; leucine=“L” or “Leu”;lysine “K” or “Lys”; methionine=“M” or “Met”; phenylalanine=“F” of“Phe”; proline “P” or “Pro”; serine=“S” or “Ser”; threonine=“T” or“Thr”; tryptophan=“W” or “Trp”; tyrosine=“Y” or “Tyr”; and valine=“V” or“Val”. Amino acid sequences are written from amino to carboxy-terminus,unless otherwise indicated. Conventional nucleic acid nomenclature isalso used, wherein “A” means adenine, “C” means cytosine, “G” meansguanine, “T” means thymine, and “U” means uracil. Nucleotide sequencesare written from 5′ to 3′, unless otherwise indicated.

Conservative amino acid substitutions are well-known in the art, andinclude substitutions made on the basis of a similarity in polarity,charge, solubility, hydrophobicity and/or the hydrophilicity of theamino acid residues involved. Typical conservative substitutions arethose in which the amino acid is substituted with a different amino acidthat is a member of the same class or category, as those classes aredefined herein. Thus, typical conservative substitutions includearomatic to aromatic, apolar to apolar, aliphatic to aliphatic, acidicto acidic, basic to basic, polar to polar, etc. Other conservative aminoacid substitutions are well known in the art.

Structures

As those in the art are aware, protein structures can be of differentquality. Presently, the highest quality determination methods areexperimental structure prediction methods based on x-ray crystallographyand/or NMR spectroscopy. In x-ray crystallography, “high resolution”structures are those wherein atomic positions are determined at aresolution of about 2 Å or less, and enable the determination of thethree-dimensional positioning of each atom (or at least eachnon-hydrogen atom) of a protein. “Medium resolution” structures arethose wherein atomic positioning is determined at about the 2-4 Å level,while “low resolution” structures are those wherein the atomicpositioning is determined in about the 4-8 Å range. Herein, proteinstructures that have been determined by x-ray crystallography or NMR maybe referred to as “template polypeptides” or “experimental structures,”as compared to those determined by computational methods, i.e., derivedfrom the application of one or more computer algorithms to a primaryamino acid sequence to predict protein structure.

Accordingly, in another embodiment, a representation of athree-dimensional structure of the mPGES-1 substrate binding domain(SBD) is provided. The representation is characterized in that: a) aminoacid residues Q36, R110, T114, Y130, and Q134 of mPGES-1 are associatedwith the PGH2-binding site of the SBD; b) amino acid residue Y130 ofmPGES-1 are associated with the peroxy head of prostaglandin H2 (PGH2)when PGH2 occupies at least a portion of the binding site; c) amino acidresidue Y130 of mPGES-1 is associated with the —SH group of glutathione(GSH) when GSH occupies at least a portion of the binding site; d) aminoacid residues R110, T114, and Q36 of mPGES-1 are associated with thecarboxyl tail of PGH2; e) the calculated binding free energy (□G) for anSBD-PGH2 complex is between −5.0 kcal/mol and −9.0 kcal/mol; and f) thecalculated binding free energy (□G) for an SBD-GSH complex is between−4.0 kcal/mol and −8.0 kcal/mol.

In another embodiment, a representation of a three-dimensional structureof an mPGES-1 trimer is provided. The representation is characterized inthat: a) each monomer of the trimer comprises a representation of athree-dimensional structure of the mPGES-1 substrate binding domain(SBD); b) the trimer comprises C₃-fold symmetry; and c) therepresentation of the trimer comprises a homology model based on thecrystallographic structure of subunit 1 of cytochrome c oxidase.

As discussed throughout the specification, protein structures can bedetermined entirely by computational methods, including, but not limitedto, homology modeling, threading, and ab initio methods. Often, modelsproduced by such computational methods are “reduced” models. A “reducedmodel” refers to a three-dimensional structural model of a proteinwherein fewer than all heavy atoms (e.g., carbon, oxygen, nitrogen, andsulfur atoms) of the protein are represented. For example, a reducedmodel might consist of just the alpha-carbon atoms of the protein, witheach amino acid connected to the subsequent amino acid by a virtualbond. As will be appreciated by those in the art, more detailed modelstructures of a protein can be assembled from a reduced model. Forexample, a reduced model comprised only of amino acid residue side chaincenters of mass implicitly specifies the location of the atomscomprising the side chain, as well the position of the peptide backbone.Accordingly, whatever greater level of atomic detail is required, ifany, for the particular application can be added to a reduced model, andit is understood that once a protein structure based on a reduced modelhas been generated, all or a portion of it may be further refined toinclude additional predicted detail, up to including all atom positions.

Computational methods usually produce lower quality structures thanexperimental methods, and the models produced by computational methodsare often called “inexact models.” In contrast, the present methodsprovide a mechanism for generating precise three-dimensional structureof an mPGES-1 synthase molecule using various forms of information. Inthe present methods structural motifs from a query polypeptide can becompared to similar motifs in a homologous template polypeptide. Thecomparison can be repeatedly refined until a final conformation set isobtained. Throughout the refinement process, atomic positions of atomsin the query polypeptide can be repeatedly compared to those of thetemplate polypeptide. The differences can be quantified via a measurecalled “root mean square deviation” (RMSD). A query model having an RMSDof about 2.0 Å or less as compared to a corresponding experimentallydetermined template structure is considered “high quality”. Frequently,predicted query models have an RMSD of about 2.0 Å to about 6.0 Å whencompared to one or more experimentally determined template structures,and are called “inexact models.” As those in the art will appreciate,RMSDs can also be determined for one or more atomic positions when twoor experimental structures have been generated for the same protein.

The term “root mean square deviation” means the square root of thearithmetic mean of the squares of the deviations. It is a way to expressthe deviation or variation from a trend or object. For purposes of thisinvention, the “root mean square deviation” defines the variation in thebackbone of a template polypeptide from the backbone of a querypolypeptide or an active site portion thereof, as defined by thestructure coordinates described herein. “Having substantially the samethree-dimensional structure” refers to a polypeptide that ischaracterized by a set of atomic structure coordinates that have a rootmean square deviation (RMSD) of less than or equal to about 1.5 Å whensuperimposed onto the atomic structure coordinates of a templatepolypeptide when at least about 50% to 100% of the C-alpha atoms of thecoordinates are included in the superposition.

Slight variations in structure coordinates can be generated bymathematically manipulating the template polypeptide structurecoordinates. For example, the structure coordinates could be manipulatedby crystallographic permutations of the structure coordinates,fractionalization of the structure coordinates, integer additions orsubtractions to sets of the structure coordinates, inversion of thestructure coordinates or any combination of the above. Alternatively,modifications in the crystal structure due to mutations, additions,substitutions, and/or deletions of amino acids, or other changes in anyof the components that make up the crystal, could also yield variationsin structure coordinates. Such slight variations in the individualcoordinates will have little effect on overall shape. If such variationsare within an acceptable standard error as compared to the originalcoordinates, the resulting three-dimensional model is considered to bestructurally equivalent.

As used herein, the term “model” refers to a representation in atangible medium of the three-dimensional structure of a protein,polypeptide or peptide. For example, a model can be a representation ofthe three dimensional structure in an electronic file, on a computerscreen, on a piece of paper (i.e., on a two dimensional medium), and/oras a ball-and-stick figure. Physical three-dimensional models aretangible and include, but are not limited to, stick models andspace-filling models. The phrase “imaging the model on a computerscreen” refers to the ability to express (or represent) and manipulatethe model on a computer screen using appropriate computer hardware andsoftware technology known to those skilled in the art. Such technologyis available from a variety of sources including, for example, Evans andSutherland, Salt Lake City, Utah, and Biosym Technologies, San Diego,Calif. The phrase “providing a picture of the model” refers to theability to generate a “hard copy” of the model. Hard copies include bothmotion and still pictures. Computer screen images and pictures of themodel can be visualized in a number of formats including space-fillingrepresentations, a carbon traces, ribbon diagrams and electron densitymaps. A variety of such representations of the structural models of thepresent invention are shown, for example, in the figures. In practice,predicting the three-dimensional structure of a protein can be attemptedon various levels, ranging from purely de novo, or “ab initio,”approaches to those that incorporate constraints derived fromexperimental data.

The primary structure of a polypeptide can be defined as the sequence ofamino acid residues that comprise the polypeptide. The alpha carbon ofeach residue form the scaffold upon which the structure of thepolypeptide is built. In general, the single bond between analpha-carbon and its attached R-group provides limited rotationalfreedom. Collectively, such structural flexibility enables a number ofpossible conformations to be assumed at a given region within apolypeptide. As discussed in greater detail below, the particularconformation actually assumed depends on thermodynamic considerations,with the lowest energy conformation being preferred.

In addition to primary structure, proteins also have secondary,tertiary, and, in multi-subunit proteins, quaternary structure.“Secondary structure” refers to local conformation of the polypeptidechain, with reference to the covalently linked atoms of the peptidebonds and alpha-carbon linkages that string the amino acid residues ofthe protein together. Side chain groups are not typically included insuch descriptions. Representative examples of secondary structuresinclude alpha-helices, parallel and anti-parallel beta structures, andstructural motifs such as helix-turn-helix, the leucine zipper, the zincfinger, the beta-barrel, and the immunoglobulin fold. Movement of suchdomains relative to each other often relates to biological function and,in proteins having more than one function, different binding or effectorsites can be located in different domains.

“Tertiary structure” concerns the overall three-dimensional structure ofa protein, including the spatial relationships of amino acid residueside chains and the geometric relationship of different regions of theprotein. “Quaternary structure” relates to the structure andnon-covalent association of different polypeptide subunits in amultisubunit protein, such as a trimer.

Modulators of mPGES-1 Activity

As described above, molecular modeling involves the use of computationalmethods, preferably computer assisted methods, to build realistic modelsof query polypeptides that are identifiably related in sequence to atemplate polypeptide having a known crystal structure. The presentinvention also includes the use of molecular and computer modelingtechniques to design and select ligands, such as small molecule agonistsor antagonists or other compounds that interact with mPGES-1 molecules.The methods utilized in ligand modeling range from molecular graphics(i.e., three-dimensional representations) to computational chemistry(i.e., calculations of the physical and chemical properties) to makepredictions about the binding of ligands or activities of ligands; todesign new ligands; and to predict novel molecules, including ligandssuch as compounds that inhibit the activity of a synthase, such amPGES-1.

According to the present invention, a “cognate ligand” of a mPGES-1protein is any protein that interacts with or more particularly, bindsto, a mPGES-1 protein in nature (e.g., under any normal, natural, orphysiological conditions in vitro or in vivo). As such, the term“cognate” is intended to refer to the relationship in nature betweenmPGES-1 and other ligands. The term ligand is intended to generically orgenerally refer to any ligand, binding partner, corepressor, substrate(such terms being capable of use interchangeably) or other protein orcompound with which the SBD of mPGES-1 interacts. As such, the termimplies any interaction relationship between mPGES-1 and anothercompound.

The structures used to perform the above-described method have beendescribed in detail above and in the Examples section, and include anystructural homologues of proteins described herein. According to thepresent invention, the phrase “models that define the three dimensionalstructure” is defined as any means of obtaining, providing, supplying,accessing, displaying, retrieving, or otherwise making available themodels defining any three dimensional structures as described herein.For example, the step of providing can include, but is not limited to,accessing the structure from a database or other source; importing thestructure into a computer or other database; displaying the model of thestructure in any manner, such as on a computer, on paper, etc.; anddetermining the three dimensional structure described by the presentinvention de novo using the guidance provided herein.

Methods of structure based identification of compounds of the presentinvention include identifying a candidate compound for interacting withan SBD in mPGES-1, represented by the structure model, by performingstructure based drug design with the model of the structure. Accordingto the present invention, the step of “identifying” can refer to anyscreening process, modeling process, design process, or other process bywhich a compound can be selected as useful for binding or inhibiting theactivity of protein or complex according to the present invention.Methods of structure-based identification of compounds are described indetail throughout the specification.

Structure based identification of compounds (e.g., structure based drugdesign, structure based compound screening, or structure based structuremodeling) refers to the prediction or design of a conformation of apeptide, polypeptide, protein, or to the prediction or design of aconformational interaction between such protein, peptide or polypeptide,and a candidate compound, by using the three dimensional structure ofthe peptide, polypeptide or protein. Typically, structure basedidentification of compounds is performed with a computer (e.g.,computer-assisted drug design, screening or modeling). For example,generally, for a protein to effectively interact with (e.g., bind to) acompound, it is necessary that the three dimensional structure of thecompound to assume a compatible conformation that allows the compound tobind to the protein in such a manner that a desired result is obtainedupon binding. Knowledge of the three dimensional structures of thecomponents of the complexes described herein in the conformation inwhich they bind to one another enables a skilled artisan to design acompound having such compatible conformation, or to select such acompound from available libraries of compounds and/or structuresthereof. For example, knowledge of the three dimensional structure ofthe substrate binding domain of mPGES-1 enables one of skill in the artto design or select a compound structure that is predicted to bind tothe SBD of mPGES-1 at that site and result in, for example, inhibitionof the binding of mPGES-1 to its natural ligand. Similarly, one candesign or select (identify) a compound that has the opposite, orstimulatory effect on the complex components.

Suitable structures and models useful for structure based drug designare disclosed herein. Preferred target structures, such as the mPGES-1substrate binding domain or mPGES-1 trimer, include any representationof the structure produced by any modeling method disclosed herein.

According to the present invention, the step of identifying, selectingor designing a compound for testing in a method of structure basedidentification of the present invention can include creating a newchemical compound structure or searching databases of libraries of knowncompounds (e.g., a compound listed in a computational screening databasecontaining three dimensional structures of known compounds). Designingcan also be performed by simulating chemical compounds having substitutemoieties at certain structural features. The step of designing caninclude selecting a chemical compound based on a known function of thecompound. Chemical compounds can generally include any peptide,oligonucleotide, carbohydrate and/or synthetic organic molecule. Apreferred step of designing comprises computational screening of one ormore databases of compounds in which the three-dimensional structure ofthe compound is known and is interacted (e.g., docked, aligned, matched,interfaced) with the three dimensional of a mPGES-1 molecule providedherein by computer (e.g. as described by Humblet and Dunbar, AnimalReports in Medicinal Chemistry, vol. 28, pp. 275-283, 1993, M Venuti,ed., Academic Press). The compound itself, if identified as a suitablecandidate by the method of the invention, can be synthesized and testeddirectly with one or more of the components of an mPGES-1 molecule, or amolecule-ligand complex, for example, in a biological assay. Methods tosynthesize suitable chemical or protein-based compounds are known tothose of skill in the art and depend upon the structure of the chemicalbeing synthesized. Methods to evaluate the bioactivity of thesynthesized compound depend upon the bioactivity of the compound (e.g.,inhibitory or stimulatory) and are discussed herein.

In a molecular diversity strategy, large compound libraries aresynthesized, for example, from peptides, oligonucleotides, carbohydratesand/or synthetic organic molecules, using biological, enzymatic and/orchemical approaches. The critical parameters in developing a moleculardiversity strategy include subunit diversity, molecular size, andlibrary diversity. The general goal of screening such libraries is toutilize sequential application of combinatorial selection to obtainhigh-affinity ligands for a desired target, and then to optimize thelead molecules by either random or directed design strategies.

In the present method of structure based identification of compounds, itis not necessary to align the structure of a candidate chemical compound(i.e., a chemical compound being analyzed in, for example, acomputational screening method of the present invention) to each residuein a target site (target sites will be discussed in detail below).Suitable candidate chemical compounds can align to a subset of residuesdescribed for a target site. For example, a subset of residues caninclude amino acid residues Q36, R110, T114, Y130, and Q134, positionedin the PGH2-binding site of an mPGES-1 molecule. Preferably, a candidatechemical compound comprises a conformation that promotes the formationof covalent or non-covalent cross-linking between the target site andthe candidate chemical compound. In one aspect, a candidate chemicalcompound binds to a surface adjacent to a target site to provide anadditional site of interaction in a complex. When designing anantagonist (e.g., a chemical compound that inhibits the biologicalactivity of a mPGES-1 molecule), for example, the antagonist should bindwith sufficient affinity to the target binding site or substantiallyprohibit a ligand (e.g., a molecule that specifically binds to thesubstrate binding domain) from binding to a target site. It will beappreciated by one of skill in the art that it is not necessary that thecomplementarity between a candidate chemical compound and a target siteextend over all residues specified here in order to inhibit or promotebinding of a ligand.

One embodiment of the present invention for structure based drug designcomprises identifying a compound (e.g., a chemical compound) thatcomplements the shape of a component of an mPGES-1 molecule-PGES-1substrate complex, including a portion of mPGES-1 (including, but notlimited to, an mPGES-1 trimer. Such method is referred to herein as a“geometric approach”. In a geometric approach, the number of internaldegrees of freedom (and the corresponding local minima in the molecularconformation space) is reduced by considering only the geometric(hard-sphere) interactions of two rigid bodies, where one body (theactive site) contains “pockets” or “grooves” that form binding sites forthe second body (the complementing molecule, such as a ligand).

The geometric approach is described by Kuntz et al., J. Mol. Biol.,1982, 161:269-288, which is incorporated by this reference in itsentirety. The algorithm for chemical compound design can be implementedusing the software program DOCK Package, Version 1.0 (available from theRegents of the University of California). Pursuant to the Kuntzalgorithm, the shape of the cavity or groove on the surface of astructure at a binding site or interface is defined as a series ofoverlapping spheres of different radii. One or more extant databases ofcrystallographic data (e.g., the Cambridge Structural Database Systemmaintained by University Chemical Laboratory, Cambridge University,Lensfield Road, Cambridge CB2 1EW, U.K.) or the Protein Data Bankmaintained by Brookhaven National Laboratory, is then searched forchemical compounds that approximate the shape thus defined. Chemicalcompounds identified by the geometric approach can be modified tosatisfy criteria associated with chemical complementarity, such ashydrogen bonding, ionic interactions or Van der Waals interactions.

Another embodiment provides for structure based identification ofcompounds comprises determining the interaction of chemical groups(“probes”) with an active site at sample positions within and around abinding site or interface, resulting in an array of energy values fromwhich three dimensional contour surfaces at selected energy levels canbe generated. This method is referred to herein as a “chemical-probeapproach.” The chemical-probe approach to the design of a chemicalcompound of the present invention is described by, for example,Goodford, J. Med. Chem., 1985, 28:849-857, which is incorporated by thisreference herein in its entirety, and is implemented using anappropriate software package, including for example, GRID (availablefrom Molecular Discovery Ltd., Oxford OX2 9LL, U.K.). The chemicalprerequisites for a site-complementing molecule can be identified at theoutset, by probing the substrate binding domain (SBD) of an mPGES-1molecule with different chemical probes, e.g., water, a methyl group,amine nitrogen, carboxyl oxygen and/or a hydroxyl. Preferred sites forinteraction between an active site and a probe are determined. Putativecomplementary chemical compounds can be generated using the resultingthree dimensional patterns of such sites.

According to the present invention, suitable candidate compounds to testusing the method of the present invention include proteins, peptides orother organic molecules, and inorganic molecules. Suitable organicmolecules include small organic molecules. Peptides refer to smallmolecular weight compounds yielding two or more amino acids uponhydrolysis. A polypeptide is comprised of two or more peptides. As usedherein, a protein is comprised of one or more polypeptides. Preferredtherapeutic compounds to design include peptides composed of “L” and/or“D” amino acids that are configured as normal or retroinverso peptides,peptidomimetic compounds, small organic molecules, or homo- orhetero-polymers thereof, in linear or branched configurations. Suitablecompounds for design or identification are described in detail below.

A compound that is identified by the method of the present invention canoriginate from a compound having chemical and/or stereochemicalcomplementarity with a site on one or more components of a SBD of amPGES-1 molecule as described herein. Such complementarity ischaracteristic of a compound that matches the surface of the protein(s)either in shape or in distribution of chemical groups and binds toprotein(s) to regulate (e.g., by inhibition or stimulation/enhancement)binding of a mPGES-1 molecule to one or more of its cognate ligands, forexample, or to otherwise modulate the biological activity of mPGES-1.

The following general sites of amino acid residues Q36, R110, T114,Y130, and Q134, positioned in the PGH2-binding site are targets forstructure based drug design or identification of candidate compounds andlead compounds (also referred to herein as target sites or activesites), although other sites may become apparent to those of skill inthe art based on the three-dimensional structures provided herein.Although many of the sites described below are illustrated with respectto the specific amino acid sequence of a particular mPGES-1 moleculebecause the tertiary structures are predicted to be highly similar inhomologous target sites on other highly related proteins and complexes(e.g., the homologous protein in different mammalian species; differentmPGES-1 proteins that are structurally related) it is to be understoodthat the description of the target sites is intended to encompass allother such homologues of the exemplified sequences and structures. Oneof skill in the art can readily extrapolate the amino acid residueswithin a sequence described herein to the corresponding amino acidresidues in a highly related sequence simply by aligning the relatedsequences. More specifically, one of skill in the art can readilydetermine whether a given sequence aligns with another sequence, as wellas identify conserved regions of sequence identity or homology withinsequences, by using any of a number of software programs that arepublicly available. For example, one can use BLOCKS (GIBBS) and MAST(Henikoff et al., Gene, 1995, 163:17-26; Henikoff et al., Genomics 1994,19:97-107), typically using standard manufacturer defaults.

Exemplary target sites include, but are not limited to: (1) amino acidresidues Q36, R110, T114, Y130, and Q134, positioned in the PGH2-bindingsite; (2) amino acid residue Y130 in proximity to the peroxy head ofPGH2 and the —SH group of GSH in the binding site; around residue Y130of mPGES-1, reflecting the distinct role of Y130 residue; (3) themPGES-1-catalyzed reaction of PGH2 can be initialized by theelectrophilic attack of the —SH group of GSH at the peroxy oxygen ofPGH2; and (4) amino acid residues R110, T114, and Q36 contact thecarboxyl tail of PGH2. These target sites are described in detail in theExamples and the Figures. Combinations of any of these general sites arealso suitable target sites. These sites are generally referenced withregard to the tertiary structure of the sites. Even if some of suchsites were generally known or hypothesized to be important sites priorto the present invention, the present invention actually defines thesites in three dimensions and confirms or newly identifies residues thatare important targets that could not be confirmed or identified prior tothe present invention. The use of any of these target sites as a threedimensional structure is novel and encompassed by the present invention.Many of these target sites are further described below and illustratedin the Figures and Examples of the invention.

The potential, predicted inhibitory agonist, inhibitory antagonist, orbinding effect of a ligand or other compound on mPGES-1 molecules, suchas the substrate binding site and/or mPGES-1 trimers, may be analyzedprior to its actual synthesis and testing by the use of computermodeling techniques. If the theoretical structure of the given compoundsuggests insufficient interaction and association between it and themPGES-1 molecules, synthesis and testing of the compound may beobviated. However, if computer modeling indicates a strong interaction,the molecule may then be synthesized and tested for its ability tointeract with mPGES-1 molecules. In this manner, synthesis ofinoperative compounds may be avoided. In some cases, inactive compoundsare synthesized predicted on modeling and then tested to develop a SAR(structure-activity relationship) for compounds interacting with aspecific region of mPGES-1 molecules, such as the substrate bindingsite, or a multimer of mPGES-1, such as an mPGES-1 trimer.

One skilled in the art may use one of several methods to screen chemicalentities fragments, compounds, or agents for their ability to associatewith mPGES-1 molecules. This process may begin by visual inspection of,for example, the active site based on the atomic coordinates of thepolypeptide or the polypeptide complexed with a ligand. Selectedchemical entities, compounds, or agents may then be positioned in avariety of orientations, or docked within an individual binding pocketof mPGES-1 molecules. Docking may be accomplished using software-such asQuanta and Sybyl, followed by energy minimization and molecular dynamicswith standard molecular mechanics forcefields, such as CHARMM and AMBER.

The use of software such as GRID, a program that determines probableinteraction sites between probes with various functional groupcharacteristics and the macromolecular surface, is used to analyze thesurface sites to determine structures of similar inhibiting proteins orcompounds. The GRID calculations, with suitable inhibiting groups onmolecules (e.g., protonated primary amines) as the probe, are used toidentify potential hotspots around accessible positions at suitableenergy contour levels. The program DOCK may be used to analyze an activesite or ligand binding site and suggest ligands with complementarysteric properties. See also, Kellenberger et al., Proteins, 2004,54:671-80; Oldfield, 2003, Methods Enzymol. 374:271-300; Richardson etal., 2003, Methods Enzymol. 374:385-412; Terwilliger, 2003, ActaCrystallogr D Biol Crystallogr. 59:1174-82; Toerger and Sacchettini,2003, Methods Enzymol. 374:244-70; von Grotthuss et al., 2004, Science304:1597-9; Rajakiannan et al., 2004, J Synchrotron Radiat. 11:358-62;Claude et al., 2004, Nucleic Acids Res. 32:W606-9; Suhre and Sanejouand,2004, Nucleic Acids Res. 32:W610-4.

Once a compound that associates with mPGES-1 molecules has beenoptimally selected or designed, as described above, substitutions maythen be made in some of its atoms or side groups in order to improve ormodify its binding properties. Generally, initial substitutions areconservative, i.e., the replacement group will have approximately thesame size, shape, hydrophobicity and charge as the original group. Itshould, of course, be understood that components known in the art toalter conformation may be avoided. Such substituted chemical compoundsmay then be analyzed for efficiency of fit to a mPGES-1 molecules by thesame computer methods described in detail above.

Data Storage and Retrieval

The invention encompasses machine-readable media embedded with thethree-dimensional structure of the model described herein, or withportions thereof. As used herein, “machine-readable medium” refers toany medium that can be read and accessed directly by a computer orscanner. Such media include, but are not limited to: magnetic storagemedia, such as floppy discs, hard disc storage medium and magnetic tape;optical storage media such as optical discs or CD-ROM; electricalstorage media such as RAM or ROM; and hybrids of these categories suchas magnetic/optical storage media. Such media further include paper onwhich is recorded a representation of the atomic structure coordinates,e.g., Cartesian coordinates, that can be read by a scanning device andconverted into a three-dimensional structure with an OCR.

A variety of data storage structures are available to a skilled artisanfor creating a computer readable medium having recorded thereon theatomic structure coordinates of the invention or portions thereof and/orX-ray diffraction data. The choice of the data storage structure willgenerally be based on the means chosen to access the stored information.In addition, a variety of data processor programs and formats can beused to store the sequence and X-ray data information on a computerreadable medium. Such formats include, but are not limited to, ProteinData Bank (“PDB”) format (Research Collaboratory for StructuralBioinformatics; Cambridge Crystallographic Data Centre format;Structure-data (“SD”) file format (MDL Information Systems, Inc.; Dalbyet al., J. Chem. Inf. Comp. Sci., 1992, 32:244-255), and line-notation,e.g., as used in SMILES (Weininger, J. Chem. Inf. Comp. Sci., 1988,28:31-36). Methods of converting between various formats read bydifferent computer software will be readily apparent to those of skillin the art, e.g., BABEL (v. 1.06, Walters & Stahl, ©1992, 1993, 1994).All format representations of the polypeptide coordinates describedherein, or portions thereof, are contemplated by the present invention.By providing computer readable medium having stored thereon the atomiccoordinates of the invention, one of skill in the art can routinelyaccess the atomic coordinates of the invention, or portions thereof, andrelated information for use in modeling and design programs, describedin detail below.

While Cartesian coordinates are important and convenient representationsof the three-dimensional structure of a polypeptide, those of skill inthe art will readily recognize that other representations of thestructure are also useful. Therefore, the three-dimensional structure ofa polypeptide, as discussed herein, includes not only the Cartesiancoordinate representation, but also all alternative representations ofthe three-dimensional distribution of atoms. For example, atomiccoordinates may be represented as a Z-matrix, wherein a first atom ofthe protein is chosen, a second atom is placed at a defined distancefrom the first atom, a third atom is placed at a defined distance fromthe second atom so that it makes a defined angle with the first atom.Each subsequent atom is placed at a defined distance from a previouslyplaced atom with a specified angle with respect to the third atom, andat a specified torsion angle with respect to a fourth atom. Atomiccoordinates may also be represented as a Patterson function, wherein allinteratomic vectors are drawn and are then placed with their tails atthe origin. This representation is particularly useful for locatingheavy atoms in a unit cell. In addition, atomic coordinates may berepresented as a series of vectors having magnitude and direction anddrawn from a chosen origin to each atom in the polypeptide structure.Furthermore, the positions of atoms in a three-dimensional structure maybe represented as fractions of the unit cell (fractional coordinates),or in spherical polar coordinates.

Additional information, such as thermal parameters, which measure themotion of each atom in the structure, chain identifiers, which identifythe particular chain of a multi-chain protein in which an atom islocated, and connectivity information, which indicates to which atoms aparticular atom is bonded, is also useful for representing athree-dimensional molecular structure.

Accordingly, also provided herein is a machine-readable data storagemedium including a data storage material encoded with machine readabledata which, when using a machine programmed with instructions for usingthe data, displays a graphical three-dimensional representation of amPGES-1 molecule.

Structure information, typically in the form of the atomic structurecoordinates, can be used in a variety of computational or computer-basedmethods to, for example, design, screen for and/or identify compoundsthat bind the crystallized polypeptide or a portion or fragment thereof,or to intelligently design mutants that have altered biologicalproperties, and the like. Such modeling includes, but is not limited to,drawing pictures of the actual structures, building physical models ofthe actual structures, and determining the structures of relatedsubunits and /ligand and subunit/ligand complexes using the coordinates.Such molecular modeling can utilize known X-ray diffraction molecularmodeling algorithms or molecular modeling software to generate atomiccoordinates corresponding to the three-dimensional structure of anmPGES-1 molecule.

The information can be included in an information storage, manipulationand retrieval system, such as a computer system. Such a system caninclude a representation of the three-dimensional structure of anmPGES-1 molecule of the invention, such as a monomer, substrate bindingdomain or trimer. Generally such a system includes a user interface toview the representation.

Also provided herein are methods for conducting a biotechnologybusiness. The methods include identifying one or more candidatecompounds for regulation of interactions of mPGES-1 with its cognateligands by a method described herein. The business method furtherincludes generating a machine-readable medium, or data signal embodiedin a carrier wave, embedded with information that corresponds to thethree-dimensional structural representation of the candidate compoundand providing the medium or data signal to an end user.

The following examples are provided to illustrate the practice ofpreferred embodiments of the instant invention, and in no way limit thescope of the invention.

EXAMPLES

In order to understand the molecular mechanism of the substrate binding,an “ab initio” structure prediction approach has been developed in thepresent study to build a three-dimensional model of thesubstrate-binding domain (SBD) of mPGES-1 by making use of thestructural information available for both mPGES-1 and MGST1 of the MAPEGsuperfamily. Based on the three-dimensional model of the SBD, keyresidues that are crucial for the substrate binding have been identifiedthrough further structural analysis and molecular docking. Moleculardocking is generally defined as the relative positioning of two or moreinteracting bodies. Such a positioning can be done by means of complexalgorithms to match physical properties of the multiple bodies or by asimple procedure such as visual analysis. Accurate and intuitivedocking, with easy and accessible information of the docking process, isan important process in the development of pharmaceuticals and novelmaterials as well as in understanding the properties of existingsystems.

Site-directed mutagenesis and catalytic activity assays have beenperformed to validate the predicted three-dimensional SBD model of thewild-type mPGES-1 and its mutants. The overall agreement between thecomputational and experimental results demonstrates some importantstructural features of the SBD of mPGES-1 and it's binding with thesubstrates, providing a basis for structure-based design of compoundsthat interact with the SBD.

“Ab initio” Structure Prediction: The sequence alignment between MGST-1and mPGES-1 (see FIG. 1) was generated by using ClusterW with the Blosumscoring function. The best alignment was selected according to not onlythe alignment score, but also the reciprocal position of the conservedresidues. These included the conserved FANPED motif at amino acidpositions #44 to #49, VERXXRAH motif from position #65 to #72 and R110.There was a gap of four residues from #55 to #58. The total homology is73%, with the sequence identity of 38.8%. The membrane-spanning regionswere defined based on the analysis of amino acids distribution and thehomology with MGST1. The locations of substrates PGH2 and GSH in the SBDof mPGES-1 were thought to be similar to the corresponding locations ofthe substrates in the SBD of MGST1 revealed by the electron density mapof MGST1 (Schmidt-Krey et al., EMBO J., 19:6311-6316; Holm et al.,Biochem. Biophys. Acta, 1594:276-285) Considering the low-resolutionquaternary structure of mPGES-1, the “ab initio” rationale (see FIG. 10)began with the construction of topological model in which each helix wasrepresented by C-alpha atoms, according to the structural parametersderived from the reported two-dimensional and three-dimensional electronprojection maps of mPGES-121 and MGST1 and shown in Table 1 below:

Helix A B C D Helix center (x,y plane) 11.0Å, 16.0Å 9.0Å, 2.0Å 0.0Å,0.0Å 19.0Å, 10.0Å Tilt angle(θ) 27.0°~37.0° 12.0°~20.0° 12.0°~22.0°18.0°~18.0° Helix-between distance A-C: 19.4Å B-C: 9.2Å D-C: 21.5Å Helixarrangement Anti-clockwise Membrane thickness 26.0 Å Kink of helix Ctoward helix A  3.0 Å Kink point to the C-terminal of helix C 11.0 ÅMotion along the membrane normal (z axis) ±5.0 Å Relative rotation ofeach helix ±180.0° Self-rotation of each helix ±180.0° Orientation ofhydrophobic residues Toward membrane

Here, the considered SBD is composed of alpha-helices A, C, and D fromone monomer and alpha-helix B from another neighboring monomer. Theorientation of each alpha-helix was explored along three degrees offreedom, including the relative motion along the normal to the membrane,relative rotation among helices, and the helical self-rotation. A set of144784 candidate topological (C-alpha) structures were generated andsubsequently transformed into the corresponding residues of each helix.A set of criteria (Table 1) were used to screen the candidate structuresand only 1934 candidate structures were kept for further consideration.The structures of these 1934 candidates were then fully optimized byperforming energy minimization using the Sander module of Amber7.0program suite. Initially, the loop between alpha-helices C and D was notconsidered. The energy minimization was carried out by using anon-bonded cutoff of 10 Å and conjugate gradient method, first withfixed backbone for 500 steps and then with constrained side chains for300 steps. This was followed by energy minimization on the wholemolecule for 1000 steps. Further energy minimization was performed afteradding the loop between alpha-helices C and D. The energy minimizationwas continued until the root-mean square deviation (RMSD) of the energygradient was smaller than 0.001 kcal mol⁻¹ Å⁻¹. Additional geometricscreening was based on the structural compatibility among all thehelices, as well as the overall deviation of the C-alpha atoms from theinitial positions. This process eventually resulted in a set of 27candidate structures (conformations) with some structural diversity andclosely related low energies. These sets of molecular structures wereviewed as the most possible conformations of the SBD of mPGES-1 and wereused in further molecular docking tests.

Molecular Docking and Mutational Calculation: The two native substratesPGH2 and GSH were treated as ligands, and were separately docked intothe aforementioned 27 candidate structures of the SBD of mPGES-1 byusing the AutoDock 3.0.5 program. The atomic charges used for these twoligands were the electrostatic potential (ESP)-fitted charges determinedby performing first-principles electronic structure calculations usingGaussian03 program at the HF/6-31G* level. The similar ESP-fittingcalculations based on the first-principles electronic structure methodwere used in previous computational studies of other protein-ligandsystems and led to satisfactory binding structures. The moleculardocking was performed with a large population of randomly sampled ligandconformations and with random molecular translations using the Lamarkiangenetic algorithm (LGA). Through three types of operations in the LGAmethod, namely selection, mutation, and crossover, the substrate-enzymematching quality was monitored and improved. On each docking site, theligand conformation was searched by using the Solis and Wets localsearch method in order to sample all the possible ligand conformations.Among a series of docking parameters, the size of the grid, in whichboth the enzyme and the ligand were embedded, was set to be 60 Å×60 Å×60Å along the x, y, and z directions. This size of grid is large enough tocover all the protein atoms near the docking site, and is alsosufficient for calculating the long-range electrostatic interactionsbetween the enzyme and ligand molecules. All the complex candidates wereevaluated and ranked in terms of the binding free energies by using thestandard energy score function implemented in the docking program andthe geometric matching quality. The best complex candidate was selectedfrom the docked structures according to the best geometric matching andthe low binding free energy (high binding affinity). As the enzymestructure was kept rigid in the above docking process, the structure ofthis selected complex candidate was further refined through the energyminimization using the aforementioned Amber7.0 program, leading to theconstruction of the final complex structure.

Residue-based analysis was carried out for the obtained complexstructure. Critical atomic contacts between the substrates and theenzyme were identified and the identified crucial residues binding withPGH2 include Q36, R110, T114, Y130, and Q134. In order to estimateindividual contributions from these residues to the binding affinitywith PGH2 and to know their possible role in the binding with the secondsubstrate GSH, the substrate-bound SBD structures of five mPGES-1mutants was further examined: Q36E, R110T, T114V, Y1301, and Q134E. Theinitial SBD structures of these mutants were generated based on thefinally refined SBD structure of the wild-type by using the InsightIIprogram (version 2002, Accelrys, San Diego, Calif.). The initial SBDstructures of the substrate-bound mPGES-1 mutants were energy-minimizedby using the same method as used for the substrate-bound wild-typemPGES-1. The substrate binding free energy (ΔG) with each mutant wascalculated in the same way as we did for the binding with the wild-typeenzyme. All the computations were performed on a supercomputer(Superdome) at University of Kentucky Center for Computational Sciencesand on SGI Fuel workstations and a 34-processors IBM x335 Linux cluster.

Vector, Membrane, and Cloning of mPGES-1: PQE40 expression vector, E.coli M15 and QIAprep Spin Plasmid miniprep Kit were from QIAgene.Restriction endonucleases were from New England BioLabs. The pfupolymerase was from Stratagene. Nickel-HRP was from Kirkegaard & PerryLaboratories (Gaithersburg, Mass.), polyvinylidene fluoride (PVDF)membrane was from Millipore Corp. ECL western blotting detection systemRPN 2132 was from Amersham Life science. Oligonucleotide primers weresynthesized by MWG biotech. PGH2 and PGE2 were purchased from Caymanchemicals. Other chemicals were from Sigma. The sequence of mPGES-1 wasextracted from Genebank (access No. AF27740). The specificoligonucleotide primers to full length of mPGES-1 were synthesized toincorporate restriction sites (BamHI and HindIII) into the 5′ and 3′ends of the products. PCR was performed with 2 units Taq polymerase, 1μl human placenta cDNA library. The PCR product was subcloned into E.coli expression vector plasmid PQE40 at BamH I and Hind III sites, whichwould express histidine X6 tagged mPGES-1. The ligated plasmids weretransformed into XLI-Blue competent cells with the insertion confirmedby DNA sequencing.

Site-Directed Mutagenesis of mPGES-1: The internal primers were designedto contain sense and antisense mutagenic factors with mismatched codonsin the wild-type sequence. All the mutations of mPGES-1 cDNA wereperformed by quick change site-directed mutagenesis method. Thesequences of oligonucleotides used for mutagenesis were:

Q36E: (SEQ ID NO: 1) 5′-GTGGCCATCATCACGGGCGAAGTGAGGCTGCGGAAGAAG, and(SEQ ID NO: 2) 5′-CTTCTTCCGCAGCCTCACTTCGCCCGTGATGATGGCCAC; R110T:(SEQ ID NO: 3) 5′-CTGGTCTTCCTCGTGGGCACTGTGGCACACACCGTGGCC, and(SEQ ID NO: 4) 5′-GGCCACGGTGTGTGCCACAGTGCCCACGAGGAAGACCAG; T114V:(SEQ ID NO: 5) 5′-GTGGGCCGTGTGGCACACGTCGTGGCCTACCTGGGGAAG, and(SEQ ID NO: 6) 5′-CTTCCCCAGGTAGGCCACGACGTGTGCCACACGGCCCAC; Y130I:(SEQ ID NO: 7) 5′-CCCATCCGCTCCGTGACCATCACCCTGGCCCAGCTCCCC, and(SEQ ID NO: 8) 5′-GGGGAGCTGGGCCAGGGTGATGGTCACGGAGCGGATGGG; Q134E:(SEQ ID NO: 9) 5′-GTGACCTACACCCTGGCCGAGCTCCCCTGCGCCTCCATG, and(SEQ ID NO: 10) 5′-CATGGAGGCGCAGGGGAGCTCGGCCAGGGTGTAGGTCAC;where the underlines indicate the bases that were changed. Pfu DNApolymerase was used for PCR. The PCR products were treated with DpnIendonuclease to digest the parental DNA template. All the mutantplasmids were transformed into XLI-Blue cells to amplify DNA. The DNAsequences of mutants were confirmed by sequencing.

Expression and Preparation for the Membrane Fraction of mPGES-1 and itsMutants in E. coli: The wild-type mPGES-1 and its mutant plasmids fromXLI-Blue were transformed into M15 E. coli cells. Cells were grown in500 ml TB media containing 100 μg/ml ampicillin and 25 μg/ml kanamycinat 37° C. with shaking at 270 rpm until OD reached 0.8. IPTG was addedto a final concentration of 2 mM and cells were allowed to grow foradditional 3 hours at 37° C. Cells were then harvested by centrifugationat 5000 g for 15 min at 4° C. The cell pellet was re-suspended in 15 mMTris-HCl pH 8.0 containing 0.25 M sucrose, 0.1 mM EDTA and 1 mM reducedform glutathione. The cells were broken by sonication, and then the celllysate was cleared by centrifugation at 12,500 g for 10 min. Thesupernatant then was centrifugated at 250,000 g 4° C. for 1 hour and themembrane pellet were re-suspended in PPGEG buffer (10 mM potassiumphosphate, pH 7.0, 20% glycerol, 0.1 mM EDTA and 1 mM reduced formglutathione). Total protein concentration of the membrane fraction wasdetermined by coomassie protein assay according to the manufacture'sinstruction (Bio-Rad) with BSA as a standard.

SDS-PAGE and Western Blotting: The E coli membranes (50 μg) expressingthe His-tagged wild-type and mutant mPGES-1 were subjected to SDS-PAGEon 15% polyacrylamide gel. The proteins were then electrophoreticallytransferred onto PVDF membranes. The membrane was blocked with 5% nonfatmilk in TBS (30 mM Tris-HCl, pH 7.4 containing 120 mM NaCl) at roomtemperature for 1 hour. After incubation 2 hours at room temperaturewith Nickel-HRP (1:500) in 5% nonfat milk in TBS, the membrane waswashed 3 times with TBS containing 0.1% Tween 20. The immunoreactivebands were detected with ECL plus western blotting detection system.

Activity Assay for Wild-type mPGES-1 and Its Mutants: Assays for mPGES-1activity were performed on ice in 1.5 ml microfuge tubes using PGH2 assubstrate. The reaction mixture (100 μl) contained: 100 mM sodiumphosphate, pH 7.2, 2.5 mM GSH and enzyme preparation. The reaction wasinitiated by the addition of 15 μM PGH2 from 20-fold concentrated stocksolution in dry ethanol. After 8 min of incubation on ice, the reactionwas quenched by the addition of 100 μl (2 mg/ml) SnCl2 which rapidlyreduced un-reacted PGH2 to PGF2a. The non-enzymatic conversion of PGH2to PGE2 was performed using PPGEG buffer devoid of enzyme. The reactioncontents were 1:2500 diluted, from which 50 μl aliquot was used forquantification of PDE2 concentration by EIA assay. The mPGES-1 activitywas calculated using enzymatic conversion of PGH2 to PGE2 from totalconversion subtracted by non-enzymatic conversion. When the saturationkinetics for PGH2 was determined, the activity was assayed with a fixedconcentration of 2.5 mM GSH and 1-500 μM PGH2. The KM values ofwild-type mPGES-1 and its mutants were calculated by using the GraphPadPrism 4.01 program.

Results: Structural Models of the SBD of mPGES-1: The amino acidsequence alignment of mPGES-1 with MGST1 (FIG. 1) shows that fourregions with high homology (>70%) can be assigned to four alpha-helices.These are alpha-helix A from sequence position #11 to #38, alpha-helix Bfrom #78 to #93, alpha-helix C from #96 to #114, and alpha-helix D from#126 to #147. The longest loop between alpha-helix A and alpha-helix Bcontains typically conserved motifs. According to the geometricparameters used for the alpha-helices (see Table 1, supra), the explored144784 conformations derived from the initial topological model arescreened down to 1934 candidate conformations. After the energyminimizations using the Sander module of Amber7.0 program, these 1934candidates were clustered into four groups as shown in FIG. 2. The 1232candidates in the first group have positive energies, indicating thatthese 1232 candidate conformations are energetically unfavorable andshould be excluded. The energies calculated for the other groups ofcandidates are negative and become lower and lower from group II togroup IV (see FIG. 2), showing the significant improvement of thepositions of the side chains. This funnel-like adaptation of the fouralpha-helices packing clearly shows both the energetic and geometricaspects dominating the formation of the final reasonable conformationsof mPGES-1. Such folding-mimic process (see FIG. 10) also helps toreduce the redundancy of the helix orientations. More strict geometricchecking and evaluation of the root-mean-square-deviation (RMSD) of theC-alpha positions from those in the initial topological model help usobtain eventual 27 best candidate conformations selected from group IV(see FIG. 2). Although some of the other candidate structures also hadsmall RMSD values and lower energies, those candidate structures werenot selected because the helix packing was not as good as the selected27 ones. Further, the helix packing was re-examined more strictlyaccording to the geometric criteria (see Table 1, supra) and was finelytuned for the selected 27 candidates. Each of the finely tuned 27candidate structures was energy-minimized again by using the Sandermodule of Amber7.0 program until the energy gradient criterion of 0.001kcal mol⁻¹ Å⁻¹ was achieved. The finally energy-minimized 27 candidateconformations with low energies and small RMSD values (FIG. 3) can beconsidered as the most possible conformations of the SBD of mPGES-1.

Complex Model for mPGES-1 Binding with PGH2 and GSH: The first test onthe 27 structural models of the SBD of mPGES-1 was performed for theirbinding with substrates PGH2 and GSH, through molecular docking usingboth the binding site searching and interaction energy scoring. Each ofthe 27 structures was used to perform molecular docking, with PGH2 andGSH separately. The calculated binding free energies of PGH2 with theSBD of mPGES-1 range from −4.1 to −8.3 kcal/mol. The correspondingvalues of the dissociation constant (K_(d)) fall between 995 μM to 0.768μM. The range of predicted K_(d) values covers the reported experimentalvalues (˜28, ˜14, and ˜160 μM) of the Michaelis-Menten constant (K_(M)).We note that K_(d)≠K_(M) in theory. Nevertheless, K_(d)≠K_(M) under thewidely used rapid-equilibration assumption which assumes that thedissociation of the enzyme-substrate complex is much faster than thecorresponding catalytic reaction. The catalytic reaction ischaracterized by the catalytic rate constant (k_(cat)). Based on thereported low k_(cat) values (1.8 to 50 S⁻¹) for mPGES-1, K_(d)≈K_(M) insubsequent calculations and discussions. The finally selected complexmodel of mPGES-1 binding with both PGH2 and GSH substrates was the mostsatisfactory one with optimal geometric matching (see FIG. 4A) comparedto the other complex candidates. The binding free energy (AG) calculatedfor the final complex model is −7.8 kcal/mol for PGH2 and −6.0 kcal/molfor GSH, respectively. Assuming K_(d)≠K_(M), the energetic resultscalculated for the final complex model predict a K_(M) value of 2.1 μMfor PGH2 and a K_(M) value of 41.3 μM for GSH.

Based on the predicted complex model shown in FIG. 4B, substrate PGH2stays in a pocket formed by alpha-helices A, C, and D, with the twotails of PGH2 buried deeply. PGH2 has contacts with both hydrophilic andhydrophobic residues of mPGES-1. The most important interactions arearound the carboxyl group on one tail of PGH2, which is surrounded bythe polar side chain of Q36, positively charged side chain of R110, andside chain of T114 from alpha-helix C. The binding of these residueswith PGH2 is associated with a network of electrostatic and hydrogenbonding interactions. Such an interacting mode is consistent with thereported experimental finding that R110S mutant of mPGES-1 completelylost the catalytic activity. The hydroxyl group on the other tail ofPGH2 interacts with side chain of Q134 through possible hydrogenbonding, and this hydrophobic tail is surrounded by side chains of V29,V30, I33, and V37, further strengthening the binding affinity of PGH2with mPGES-1. As seen in the complex model, the two oxygen atoms formingthe peroxy bridge of PGH2 also interact with the —SH group of GSHthrough hydrogen bonding. The head of the PGH2 molecule is close to thearomatic side chain of Y130 and is covered by hydrophobic part of theside chain of K120.

For the binding of GSH with the SBD of mPGES-1, as shown in FIG. 4C, GSHis bound in a site nearby PGH2 under the loop between alpha-helices Cand D. Compared to the location of PGH2, GSH is closer to surface of theprotein. Based upon this model, another alpha-helix C in a neighboringmonomer could also be involved in the binding with GSH. The moleculardocking with GSH was also guided by the insights obtained from thereported two-dimensional and three-dimensional electro-density maps ofmPGES-1 and MGST1. Useful features of the GSH binding still can bederived from the current model. As shown in FIG. 4C, GSH is surroundedby Y80, L118, K120, L121, P124, R126, and Y130, and it is close to PGH2.Besides the thiol (—SH) group of GSH interacts with PGH2, the carboxylgroup on the Gly-end of GSH interacts with positively charged side chainof R126. Another carboxyl group on the gamma-Glu-end of GSH pointstoward the backbone of K120 and L121. The packing of the —SH group ofGSH and the head of PGH2 with the aromatic side chain of Y130 implies apossibly important role of Y130 in the catalytic function of mPGES-1.

PGH2 Binding with mPGES-1 Mutants: Based on the modeled SBD structure ofmPGES-1 and the modeled binding structures with substrates, five keyresidues (i.e. Q36, R110, T114, Y130, and Q134) involved in thePGH2-binding site were selected for mutational studies in order tofurther test the predicted SBD model of mPGES-1. According to thethree-dimensional model of the substrate binding discussed above, theenzyme binding with substrate PGH2 should be weakened by such mutationsas Q36E, R110T, T114V, Y1301, and Q134E. The binding affinities wereestimated for the mutants of mPGES-1 by using the same method as usedfor the wild-type enzyme. The calculated results are summarized in Table2 in comparison with available experimental data:

Calculated binding Experimental K_(M) (μM) ΔG K_(d) This PreviouslyEnzyme (kcal/mol) (μM) work reported Wild-type −7.8 2.1^(b) 130 14 to160 ^(a) Q36E −3.8 1600 ~1610 Q134E −4.7 359 ~734

The experimental K_(M) values reported previously by other groups (^(a))are 28 μM), 14 μM, and 160 μM. The calculated k_(d) value (^(b)) isclose to the range of the experimental K_(M) values (14 to 160 μM).

Membrane Expression of mPGES-1 and Its Mutants: Based on the informationof the structure prediction and modeling on substrate binding, fiveresidues in the PGH2-binding site of mPGES-1 were selected for furthersite-directed mutagenesis studies. The substitutions for these fiveresidues are Q36E, R110T, T114V, Y130I, and Q134E. The wild-type mPGES-1was cloned from human placenta cDNA library by PCR techniques usingspecific sense and anti-sense primers of mPGES-1. The wild-type and thefive mutants of mPGES-1 were expressed in M15 E. coli cells. As themembrane proteins are very toxic to the host E. coli, a special strategywas used to produce sufficient amount of expression in order to favorthe next activity assay. The best condition for expression was selectedas 3 hours at 37° C. The membrane fractions were further analyzed bywestern blotting using Ni-HRP as a detection system, which is moresensitive and accurate than the traditional analysis system of theprimary and secondary antibody. The results demonstrate that all thefive mutants were expressed at a level comparable with that of thewild-type enzyme (see FIG. 5).

Enzymatic Activity and Kinetic Data: The wild-type and the mutants ofmPGES-1 were assayed for the enzymatic activity in the presence of PGH2and GSH as substrates and the results are shown in FIG. 6. The R110Tmutation was designed to test mainly for its electrostatic and hydrogenbonding interactions with the carboxyl group of PGH2. This mutantretained only 17.8% catalytic activity of the wild-type, not totallyabrogated as reported by Murakami et al. (J. Biol. Chem., 2000,275:32783-32792). The T114V mutant showed 21.3% activity of thewild-type mPGES-1, which is consistent with the computational predictionthat the hydroxyl group of T114 side chain is involved in hydrogenbonding with PGH2. The Y1301 mutant lost most of the enzymatic activity,indicating that this residue cannot tolerate any amino acid change. Thissuggests that the role of Y130 in the reaction of PGH2 catalyzed bymPGES-1 is crucial. Q36E and Q134E mutants kept about 40%-50% catalyticactivity of the wild-type (FIG. 6), indicating that these two residues(Q36 and Q134) are not as important as the other three residues (R110,T114, and Y130) for the catalytic reaction.

The experimental results are listed in Table 2 and depicted in FIGS. 6and 7 for comparison with the computational predictions. As seen in FIG.6, each of the tested mPGES-1 mutants demonstrated a lower catalyticactivity compared to the wild-type, which is qualitatively consistentwith the predicted enzyme-substrate binding model. Quantitatively, theexperimental kinetic constant K_(M) was determined only for thewild-type mPGES-1 and the Q36E and Q134E mutants, but the catalyticactivity of the R110T, T114V, and Y1301 mutants is too low for themeasurement of kinetic constants. The correlation between the calculatedK_(d) and the measured K_(M) for these two mutants is represented inFIG. 8. For the wild-type mPGES-1, the experimental K_(M) value of 130μM is comparable to the K_(M) values reported by Tanikawa et al. (28 μM)(Biochem. Biophys. Res. Com., 2002, 291:884-889), Ouellet et al. (14 μM)(Protein Expr. Puri., 2002, 26:489-495), and Thoren et al. (160 μM) (J.Biol. Chem., 2003, 278:22199-22209). The calculated K_(M) value of 2.1μM is acceptable, although it is slightly smaller than the experimentalrange (14 to 160 μM). The binding constant (K_(d)) values predicted forthe Q36E and Q134E mutants are in agreement with the experimental K_(M)values, although the errors of the experimental K_(M) values determinedfor these two mutants are expected to be very large because theconcentrations of PGH2 used in the experiments (≦500 μM) are notsufficiently high due to the limitation of the solubility of PGH2. Theoverall qualitative agreement of the calculated results with theexperimental data further supports the predicted three-dimensional modelof the substrate-enzyme binding as provided herein.

Structural determination of membrane-spanning proteins is stillexceedingly difficult by experimental methods such as X-ray diffractionand NMR. As a stimulating drug target, detailed information about themPGES-1 structure and the relationship with its functions are needed. Inthe present study, this need is satisfied by performing computationalthree-dimensional structure predictions of the SBD of mPGES-1 and it'sbinding with the substrates PGH2 and GSH, followed by wet experimentaltests on the enzyme-substrate binding model predicted at atomic level.The three-dimensional model reveals key amino acid residues, includingQ36, R110, T114, Y130, and Q134, involved in the PGH2-binding site. Thisfirst three-dimensional model provides a mechanism for designing agentsthat modulate the activity of mPGES-1 by interacting with the SBDdescribed herein.

The current results (see e.g., FIGS. 6 and 7 and Table 2) obtained fromthe site-directed mutagenesis and enzymatic activity assay haveidentified two remarkable features of the predicted mPGES-1 binding withthe substrates. One is the relative positions of the peroxy head of PGH2to the —SH group of GSH in the binding site around residue Y130 ofmPGES-1, reflecting the distinct role of Y130 residue. Such a mode ofthe intermolecular interaction clearly explains why the catalyticfunction of mPGES-1 is GSH-dependent as observed in previouscharacterization studies on this enzyme. The obtained binding mode alsoindicates that the mPGES-1-catalyzed reaction of PGH2 can be initializedby the electrophilic attack of the —SH group of GSH at the peroxy oxygenof PGH2. Also provided herein are the contacts between the carboxyl tailof PGH2 and residues R110, T114, and Q36 of mPGES-1. Intermolecularinteractions on this subsite reveal the role of residues R110, T114, andQ36 in the binding of mPGSE-1 with PGH2. R110 is conserved not onlystrictly for the MGST1 subfamily, but also for the whole superfamily ofMAPEG (Jakobsson, et al., Protein Sci., 1999, 8:689-692; Jakobsson etal., Am. J. Respir. Crit. Care Med., 1996, 161:S20-S24; Ekstrom et al.,Biochem. Biophys. Acta, 2003, 1627:79-84) suggesting a similarbinding/catalytic role of this residue for all the members of thissuperfamily. The hydrogen bonding between the substrate and thesubfamily-conserved residue T114 indicates a similar role of thisresidue for the members of MGST1 subfamily in the binding with thesubstrate. The indispensable role of the substitutable conserved Y130demonstrates why mPGES-1 is specific for the reaction of PGH2. Aminoacid residues #36 and #134 are not conserved even for the MGST1subfamily, which is consistent with our observation that the catalyticactivity of mPGES-1 did not dramatically decrease when these tworesidues were mutated.

Accordingly, in one embodiment, the present combined computationalmodeling and wet experimental tests have led to establishment of athree-dimensional model of the SBD of mPGES-1 and the identification ofhow mPGES-1 binds with various substrates at the atomic level. Based onthe three-dimensional model, further computational modeling and bindingfree energy calculations were performed to evaluate the substratebinding with Q36E, R110T, T114V, Y130I, and Q134E mutants of mPGES-1,followed by the site-directed mutagenesis and catalytic activity tests.The overall agreement between the calculated and experimental resultsdemonstrates that the predicted three-dimensional model will be valuablein future rational design of potent inhibitors of mPGES-1 for novelinflammation-related therapeutics.

In another embodiment, the present studies also providethree-dimensional models of the mPGES-1 trimer. The computationalmodeling of the mPGES-1 trimer models was based on the use of thepreviously constructed three-dimensional model of the SBD (see dataprovided supra) and the use of the X-ray crystal structure of cytochromec protein.

The principle behind homology modeling is the assumption that structureis more highly conserved than sequence. This assumes an evolutionaryprocess called divergent evolution. Thus, the deduced structure of theMGST-1 trimer observed in the three-dimensional projection map(Schmidt-Krey et al., EMBO J., 2000, 19:6311-6316) shows a strikingsimilarity to subunit 1 of both bacterial and bovine cytochrome coxidase (Iwata et al., Nature, 1995, 376:660-669; Tsukihara et al.,Science, 1996, 272:1136-1144), in spite of the fact that there isneither any shared functional property nor any sequence similaritybetween MGST1 and subunit 1 of cytochrome c oxidase. In addition, thesequence of the mPGES-1 shares more than 73% of identity with MGST1sequence confirming thus, that the topology of the mPGES-1 is similar toMGST1 structure. Accordingly, in the present studies the structuralinformation previously deduced for cytochrome c oxidase was used toextrapolate information required to build three-dimensional models ofthe mPGES-1 trimer.

As previously noted, the present studies have provided athree-dimensional model of the SBD of mPGST-1. This model wassuperimposed to the subunit 1 of the template (the X-ray crystalstructure of cytochrome c oxidase, i.e. 1OCC.pdb). The samethree-dimensional model of the SBD of mPGST-1 was also superimposed tothe subunits 2 and 3 of the template. Thus, the constructed mPGES-1trimer model has three equivalent SBDs with an approximate C3-foldsymmetry. Depicted in FIG. 11 are different views of the constructedthree-dimensional model #1 (FIG. 11, panel (a), panel (b) and panel (c))of the mPGES-1 trimer complexed with GSH and PGH2.

three-dimensional model #2 (see FIG. 13) is a pure homology modelmodeled based on the template (the X-ray crystal structure of cytochromec oxidase, i.e. 1OCC.pdb). The homology model of the mPGES-1 trimer wasconstructed using the Homology module of InsightII program, based on theindividual sequence/template alignments (see FIG. 12). The alpha-helicesare underlined in the sequence alignment of mPGES-1 with the cytochromec template shown in FIG. 12. The refined final alignments based on thesecondary structure of each unit were used for constructing homologymodels of human mPGES-1 using MODELER module of InsightII program.MODELER is a well-known comparative modeling methodology, whichgenerates a refined three-dimensional homology model of a proteinsequence automatically and rapidly, based on a given sequence alignmentto a known three-dimensional protein structure.

The obtained initial three-dimensional models of the mPGES-1 trimer wererefined further through carefully performing the energy-minimizationsand constrained molecular dynamics (MD) simulations. FIG. 13 shows theobtained three-dimensional model #2 of the mPGES-1 trimer complexed withan inhibitor (i.e. MK-886 reported in literature) in each SBD.

The examples set forth above are provided to give those of ordinaryskill in the art a complete disclosure and description of how to makeand use the embodiments of the devices, systems and methods of theinvention, and are not intended to limit the scope of what the inventorsregard as their invention. Modifications of the above-described modesfor carrying out the invention that are obvious to persons of skill inthe art are intended to be within the scope of the following claims. Allpatents and publications mentioned in the specification are indicativeof the levels of skill of those skilled in the art to which theinvention pertains. All references cited in this disclosure areincorporated by reference to the same extent as if each reference hadbeen incorporated by reference in its entirety individually.

In addition, it is understood that the terminology used herein is forthe purpose of describing particular embodiments only, and is notintended to be limiting. As used in this specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the content clearly dictates otherwise. Unless defined otherwise,all technical and scientific terms used herein have the same meaning ascommonly understood by one of ordinary skill in the art to which theinvention pertains. Although any methods and materials similar orequivalent to those described herein can be used in the practice fortesting of the invention(s), specific examples of appropriate materialsand methods are described herein.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

1-25. (canceled)
 26. A method of structure-based identification ofcandidate compounds for regulation of interactions of mPGES-1 with itscognate ligands, comprising: a) providing a three dimensional structuralrepresentation of mPGES1 selected from the group consisting of: i) athree dimensional structural representation of a mPGES-1 substratebinding domain (SBD) wherein: a) amino acid residues Q36, R110, T114,Y130, and Q134 of mPGES-1 are associated with the prostaglandin H2(PGH2)-binding site of the SBD; b) amino acid residue Y130 of mPGES-1 isassociated with the peroxy head of PGH2 when PGH2 occupies at least aportion of the binding site; c) amino acid residue Y130 of mPGES-1 isassociated with the —SH group of glutathione (GSH) when GSH occupies atleast a portion of the binding site; d) amino acid residues R110, T114,and Q36 of mPGES-1 are associated with the carboxyl tail of PGH2; e) thecalculated binding free energy (ΔG) for an SBD-PGH2 complex is between−5.0 kcal/mol and −9.0 kcal/mol; and f) the calculated binding freeenergy (ΔG) for an SBD-GSH complex is between −4.0 kcal/mol and −8.0kcal/mol; and ii) a three dimensional structural representation of amPGES-1 trimer wherein: a) each monomer of the trimer comprises thethree-dimensional structural representation of the mPGES1 (SBD); b) thetrimer comprises a C₃-fold symmetry; and c) the representation of thetrimer comprises a homology model based on the crystallographicstructure of subunit 1 of cytochrome c oxidase; and b) identifying atleast one candidate compound that interacts with the three dimensionalstructural representation of a). 27-31. (canceled)
 32. A methodaccording to claim 26, wherein the step a) comprises accessing therepresentation from a database or other source; importing therepresentation into a computer or other database; and/or displaying therepresentation.
 33. A method according to claim 26, wherein the step b)comprises screening compounds from an available library and selectingthe at least one candidate compound from the screened compounds.
 34. Amethod according to claim 26, wherein the step b) comprises designing atleast one new compound and selecting the at least one candidate compoundfrom the at least one new compound.
 35. A method according to claim 26,further comprising modifying the at least one candidate compound bysubstituting at least one atom or group of atoms to provide at least onemodified compound and determining whether the at least one modifiedcompound interacts with the three dimensional structural representationof a).
 36. A method according to claim 26, further comprisingsynthesizing the at least one candidate compound and determining theactivity of the at least one candidate compound on m-PGES1 in vitro orin vivo.
 37. A method according to claim 26, wherein step b) comprisesidentifying at least one candidate compound that has complementaritywith at least one of the amino acid residues Q36, R110, T114, Y130, andQ134.
 38. A method according to claim 37, wherein step b) comprisesidentifying at least one candidate compound that has complementaritywith the amino acid residues R110, T114, and Y130.
 39. A methodaccording to claim 37, wherein step b) comprises identifying at leastone candidate compound that has complementarity with the amino acidresidues Q36, R110, and T114.
 40. A method according to claim 37,wherein step b) comprises identifying at least one candidate compoundthat has complementarity with the amino acid residues Q36, R110, T114,Y130, and Q134.
 41. A method according to claim 26, wherein in the threedimensional structural representation of the mPGES-1 SBD V29, V30, I33,and V37 are associated with the carboxyl tail of PGH2 when PGH2 occupiesat least a portion of the binding site.
 42. A method according to claim26, wherein in the three dimensional structural representation of themPGES-1 SBD K120 is associated with the peroxy head of PGH2 when PGH2occupies at least a portion of the binding site.
 43. A method accordingto claim 26, wherein in the three dimensional structural representationof the mPGES-1 SBD Y80, L118, K120, L121, P124, R126, and Y130 areassociated with GSH when GSH occupies at least a portion of the bindingsite.
 44. A method according to claim 26, wherein in the threedimensional structural representation of the mPGES-1 SBD R126 isassociated with the carboxy group on the Gly-end of GSH when GSHoccupies at least a portion of the binding site.
 45. A method accordingto claim 26, wherein in the three dimensional structural representationof the mPGES-1 SBD K120 and L121 are associated with the carboxyl groupof the gamma-Glu-end of GSH when GSH occupies at least a portion of thebinding site.
 46. A method according to claim 26, wherein in the threedimensional structural representation of the mPGES-1 SBD PGH2 interactswith GSH through hydrogen bonding between the peroxy group of PGH2 andthe —SH group of GSH.